code: def np_scatter(idx, vals, target):
      """target[idx] += vals, but allowing for repeats in idx"""
      np.add.at(target, idx, vals)
  
imports: import math
  import math
  import ssl
  from sklearn.metrics import roc_auc_score
  import torch
  import paddle
  import pgl
  import numpy
  import paddle.fluid
  import paddle.fluid.layers
  from pgl.contrib.ogb.nodeproppred.dataset_pgl import PglNodePropPredDataset
  import time
  import copy
  from ogb.nodeproppred import Evaluator
  import pickle
  from model import Proteins_baseline_model, Proteins_label_embedding_model
  from partition import RandomPartition
  from cross_edge_feat import cross_edge_feat
  from dataset.random_partition_dataset import RandomPartition
  import os
  import argparse
  from tqdm import tqdm
label_desc: Use image node_id 7 for calling a global function with example usage: np_scatter(idx, vals, target) without return types